System and method for detection of mobile device fault conditions

ABSTRACT

There is presented a system and method for detecting mobile device fault conditions, including detecting fault conditions by software operating on the mobile device. In one embodiment, the present invention provides for systems and methods for using a neural network to detect, from an image of the device, that the mobile device has a defect, for instance a cracked or scratched screen. Systems and methods also provide for, reporting the defect status of the device, working or not, so that appropriate action may be taken by a third party.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims full benefit of and priority to U.S. provisionalpatent application No. 62/329,159 filed Apr. 28, 2016 titled, “Systemand Method For Detection Of Mobile Device Fault Conditions,” and claimsfull benefit of and priority to U.S. provisional patent application No.62/464,366 filed Feb. 27, 2017 titled, “System And Method For Monitoringand Tracking Device Health”, and claims full benefit of and priority toU.S. provisional patent application No. 62/463,725 filed Feb. 26, 2017titled, “System and Method For Detection Of Display Defects” is acontinuation-in-part of U.S. non-provisional application Ser. No.15/582,471 filed 2017 Apr. 28, titled, “System and Method For DetectionOf Mobile Device Fault Conditions,” the disclosures of which are fullyincorporated herein by reference for all purposes.

FIELD AND BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates to systems and methods for detecting faultconditions in mobile devices. More particularly, the present inventionprovides for systems and methods for detecting a that a mobile devicehas a fault such as cracked, scratched, or otherwise damaged screen, andreporting the status of the screen, working or not, so that appropriateaction may be taken by a third party.

Background of the Invention

Today, the use of mobile electronic devices is widespread, andincreasing numbers of people utilize such devices in their daily lives.Examples of such devices include cellular phones, smart watches,portable digital assistants (PDAs), digital cameras, intelligent devicesfor the “Internet of things,” and laptop computers. As the growth of useof mobile devices has increased, so has the need to provide insuranceand cost-effective warranty coverage for such devices. Importantly,smart mobile devices can increase a user's productivity and quality oflife, but they are susceptible to damage from a variety of sources, suchas water damage, shock damage, and other forms of unexpected use ormisuse. It is well known that the displays on mobile devices (alsointerchangeably identified as “screens” herein), while constructed ofmore durable or tougher substances such as advanced forms of glass, arestill susceptible to fracture or breakage. When a user's mobile devicebecomes damaged, warranty replacement or insurance coverage are oftensought by the owner of a damaged device to obtain a working model.

One problem plaguing this industry is fraud. An industry has grownaround the filing of fraudulent claims for allegedly lost or stolenmobile devices. This problem is further compounded with a growing needfor insurance on the secondary mobile device market, where devices aremore likely to have a fault condition present. Countless dollars arelost each year as a result of fraudulent claims. For example, a user ofa mobile device may drop or misuse the device and crack thedisplay/screen, then attempt to apply for third party damage insuranceafter the incident in an attempt to obtain a replacement model. Also,some unscrupulous persons may claim that a device that was purchased wasreceived by post in a damaged condition, when the person actuallydamaged the device themselves. Further, a fraudulent seller may presenta picture to a customer on websites such as EBay showing a perfectlygood phone, then selling a faulty device (such as with a crackeddisplay). Even worse, collusion may occur between a mobile device ownerand a “repair” facility where an insured device is claimed damaged, andthe repair facility splits the insurance fees with the customer forfraudulently claiming devices required repair when they in fact were notfaulty.

Current systems and methods employed to check the veracity of suchclaims are not particularly sophisticated or successful in detecting anddeterring fraud, because it is difficult or impossible by currentmethods for a third to remotely assess the condition of a mobile deviceand determine whether a warranty or insurance claim is valid. Therefore,there is a need for systems and methods that overcome these and otherproblems associated with the prior art. Moreover, there is a moregeneral need to provide for systems and methods to determine that afault condition exists (or not) in a mobile device, and allow suchinformation to be reported to a third party entity.

SUMMARY OF THE INVENTION

The following technical disclosure is exemplary and explanatory only andis not necessarily restrictive of the invention as claimed.

As used herein, the term “mobile device,” “mobile electronic device,” or“device” generally refers to any electronic device capable of beingmoved from place to place, and has components (such as displays orscreens) that can become faulty or damaged. A mobile device may be astand-alone device such as a laptop computer, a desktop computer, amobile subscriber communication device, a mobile phone, a smart watch, apersonal digital assistant (PDA), a data tablet, a digital camera, avideo camera, a video game console, a media player, a global positioningsystem (GPS), Universal Serial Bus (USB) keys, mobile weapons, smartwatches or jewelry, embedded electronics, and combinations thereof. Amobile electronic device may also be any electronic device integratedwith another system or device. For example, a stereo, global positioningsystem, or other electronic device contained within a vehicle may beutilized in concert with the present invention. Software to implementmethods of the present invention can be (1) installed on, or (2)downloaded onto a mobile device indirectly or directly at any time by anauthorized user through the Internet, SMS text message, through wirelesscommunication with an app provisioning store, or in any other suitablemanner and at any suitable time for carrying out a method according tothe invention. For example, the software may be installed on the devicewhen purchased or downloaded after the device is purchased, or evenafter the device damaged or otherwise becomes faulty. The mobile devicemay be insured against loss or theft, and systems and methods of thepresent invention may operate as part of, or in addition to, aninsurance policy on the mobile device.

There is presented a system and method for detecting mobile device faultconditions, including detecting fault conditions by software operatingon the mobile device. A user that has a mobile device with an allegedfault condition (such as a cracked screen/display) may be requested toinstall an application (such as the Fault State Test Application (FSTA)referred to above) on the allegedly faulty mobile device. The FSTAprovides a trusted method to assess the veracity of a claimed faultcondition on the mobile device (such as a cracked or damagedscreen/display). The FSTA interacts with the mobile device's user toobtain sensor readings and user inputs that are utilized to assesswhether the mobile device is in fact faulty. The determination of afault/non-fault condition can then be displayed and/or sent to a thirdparty (such as an insurance company that is being asked to providepolicy coverage for the mobile device) in a secure manner (so that thetest performed on the mobile device provides a trusted result). In oneembodiment, the data sent by the FSTA in the mobile device is encryptedto prevent tampering or spoofing by the user of the mobile device, andis suitably decrypted by the recipient or software running within aserver. The FSTA in the mobile device is further configured to assist auser with capturing an image of at least a part of the mobile devicesuch as a display or casing of the mobile device, performing optionalpre-processing of the captured image, transmitting the pre-processedcaptured image to a host server, and whereupon the host server isconfigured to perform further analysis such as through use of aconvolutional neural network, wherein such analysis detects andidentifies mobile device defects from analysis of the pre-processedcaptured image received from the mobile device.

In another embodiment, a method is provided for determining that a faultcondition exists within a touch-sensitive display of a mobile device,comprising: prompting a user to touch the display; prompting the user todrag the touched point across a displayed pattern; illuminating thedisplay with a painted area to confirm areas touched by the user;measuring a plurality of pressure values measured from the user'scontact from the touched point as it is dragged across the display;determining from the plurality of pressure values whether a fault isfound in the display of the mobile device by comparing the measuredpressure values to a predetermined criterion. Any desired results may begenerated, stored in the memory of the mobile device or formatted fortransmission to a host server. The test results may comprise anyinformation obtained from the user input to the FSTA or frommeasurements of sensors by the mobile device or other data such astiming, extent of test completed or other parameters, and may furthercomprise the status of at least one fault state within the mobiledevice. Methods of the present invention may further comprise encryptingthe test results prior to transmitting the test results to the hostserver. The results of the determination, test results, and/or otherinformation may be reported to a third party, wherein the third partyincludes at least one of: the owner of the mobile device, an insuranceagency, a potential buyer, a transferee of the mobile device, a lawenforcement agency, and a lost device recovery entity. The third partymay be informed through any appropriate technique of the results of thedetermination or of any kind of test results; and in one aspect, thethird party accesses a host server to determine whether a fault stateexists within the mobile device; the fault state may comprise testresults from the mobile device.

There is also provided a method for determining that a fault conditionexists within a touch-sensitive display of a mobile device, thatincludes the steps of: prompting a user to touch a plurality of regionsthe display; illuminating the display with a painted area to confirmareas touched by the user; accumulating a plurality of magnetometerreadings from the mobile device measured when the user touches each ofthe respective areas; determining from the plurality of magnetometerreadings whether a fault is found in the display of the mobile device bycomparing the measured magnetometer readings to a predeterminedcriterion. As in other embodiments, any desired results may begenerated, stored in the memory of the mobile device or formatted fortransmission to a host server. The test results may comprise anyinformation obtained from the user input to the FSTA or frommeasurements of sensors by the mobile device or other data such astiming, extent of test completed or other parameters, and may furthercomprise the status of at least one fault state within the mobiledevice. Methods of the present invention may further comprise encryptingthe test results prior to transmitting the test results to the hostserver. The results of the determination, test results, and/or otherinformation may be reported to a third party, wherein the third partyincludes at least one of: the owner of the mobile device, an insuranceagency, a potential buyer, a transferee of the mobile device, a lawenforcement agency, and a lost device recovery entity. The third partymay be informed through any appropriate technique of the results of thedetermination or of any kind of test results; and in one aspect, thethird party accesses a host server to determine whether a fault stateexists within the mobile device; the fault state may comprise testresults from the mobile device.

An additional embodiment of the present invention provides a systemcomprising: a mobile device, the device comprising: a processor incommunication with a memory; a user interface in communication with theprocessor, the user interface including a touch-sensitive display and adata entry interface; a communications module in communication with theprocessor and configured to provide a communications interface to a hostserver, the host server further including a database; wherein the memoryof the mobile device includes instructions that when executed by theprocessor cause the mobile device to perform the steps of: prompting auser to touch the display; prompting the user to drag the touched pointacross a displayed pattern; illuminating the display with a painted areato confirm areas touched by the user; measuring a plurality of pressurevalues measured from the user's contact from the touched point as it isdragged across the display; determining from the plurality of pressurevalues whether a fault is found in the display of the mobile device bycomparing the measured pressure values to a predetermined criterion. Invarious aspects of the system embodiment, any desired results may begenerated, stored in the memory of the mobile device or formatted fortransmission to a host server. The test results may comprise anyinformation obtained from the user input to the FSTA or frommeasurements of sensors by the mobile device or other data such astiming, extent of test completed or other parameters, and may furthercomprise the status of at least one fault state within the mobiledevice. Methods of the present invention may further comprise encryptingthe test results prior to transmitting the test results to the hostserver. The results of the determination, test results, and/or otherinformation may be reported to a third party, wherein the third partyincludes at least one of: the owner of the mobile device, an insuranceagency, a potential buyer, a transferee of the mobile device, a lawenforcement agency, and a lost device recovery entity. The third partymay be informed through any appropriate technique of the results of thedetermination or of any kind of test results; and in one aspect, thethird party accesses a host server to determine whether a fault stateexists within the mobile device; the fault state may comprise testresults from the mobile device.

There is also provided a system comprising a mobile devicecommunicatively coupled to a host server. The mobile device includes aprocessor in communication with a memory; a user interface incommunication with the processor, the user interface including atouch-sensitive display and a data entry interface; a communicationsmodule in communication with the processor and configured to provide acommunications interface to the host server, the host server including aserver processor communicatively connected to a database, a servercommunications interface, a server user interface, and a server memory.The server may also be remotely accessed by a third party such as aninsurance agency, a mobile device repair agency, a mobile device seller,an advertising agent, a mobile device warranty reseller, an authorizeduser of the mobile device, and combinations thereof. The database of thehost server may maintain statistics based on analysis of imagesrespectively corresponding to the analyzed mobile devices, and may makethose statistics available for further analysis or review by any of theaforementioned third parties.

The memory of the mobile device includes instructions that when executedby the processor cause the mobile device to perform the steps of:prompting a user of the mobile device to place the mobile device so thata display section of the mobile device faces a reflective surface of amirror; performing a tracking calibration function to adjust the mobiledevice position and display for optimal image capture; modifying abrightness setting and a camera sensitivity setting of the mobile deviceto optimize image capture; determining that the mobile device is in anacceptable orientation with respect to the mirror, and thereupon,capturing an image of the mobile device as reflected from the mirror;and formatting the captured image for transmission to the server fordefect analysis.

The tracking calibration function of the present invention may beaccomplished in any desired manner, and may further comprise: retrievingone or more ambient lighting parameters from the mobile device;adjusting a brightness parameter of the display of the mobile devicebased upon the retrieved ambient lighting parameters; adjusting asensitivity parameter of the camera of the mobile device from theanalyzed ambient lighting settings; determining from camera dataobtained from the mobile device that the mobile device is out of optimalplacement, whereupon: a direction for the user to move the mobile deviceto obtain an optimal placement of the mobile device with respect to themirror is calculated; and the direction is displayed on the mobiledevice in a manner eadable by the user in the mirror.

Camera sensitivity may be adjusted to optimize placement of the mobiledevice with respect to the mirror/reflective device, and may be furtheradjusted to maximize the quality of a captured image once the mobiledevice is in acceptable position with respect to the mirror/reflectivedevice. Different operating systems of mobile devices (for example iOSand Android operating systems) may provide for varying methods toaccomplish camera sensitivity adjustment. For example, adjusting asensitivity parameter of the camera of the mobile device may furthercomprise adjusting an ISO setting of the camera and adjusting a shutterspeed of the camera. Adjusting a sensitivity parameter of the camera ofthe mobile device may also comprise adjusting an exposure compensationvalue associated with the camera of the mobile device.

The display of the mobile device may be configured to render images thatassist with determination of edges of the display in further imageprocessing. For example, one embodiment provides for displaying one ormore fiducials on the display of the mobile device; and sensing one ormore of the fiducials from camera data obtained from the mobile device.By sensing the one or more fiducials, and determining a moment,orientation, and placement of the fiducials within an image, edges ofthe display may be inferred from the detection of the location of thefiducials in the image when compared to the known rendering of thefiducials on the screen of the mobile device.

Obtaining data from a camera and/or sensors of the mobile device may beaccomplished in any desired manner. For example, retrieving one or moreambient lighting parameters from the mobile device may further compriseobtaining the parameters from an API associated with an operating systeminstalled on the mobile device.

The image captured by a camera of the mobile device may be pre-processedin any desired manner before further processing to detect defects in adisplay. For instance, formatting the captured image may furthercomprise rotating the captured image to a portrait mode; and croppingthe image. Depending on an orientation of the mobile device when thecaptured image was obtained, rotation may not be necessary. Further,rotation of the captured image may be accomplished in small amounts tode-skew the captured image. Additionally, the rotation and/or croppingmay be preferably accomplished by a processor of the mobile device, oralternatively, by the server processor of the host server once thecaptured image has been transmitted to the host server.

The captured image, with additional pre-processing such as rotation andcropping (if performed), can be formatted for transmission to the hostserver. The mobile device is communicatively coupled to the host server,for instance through a wireless communications protocol, to a mobilenetwork operator, that in turn is coupled to a network such as theinternet, which is in turn coupled to the communications interface ofthe host server. Alternatively, the mobile device may communicatedirectly through a wireless protocol to the host server, such as througha WiFi connection or a Bluetooth connection. In any event, the capturedimage may be further processed in the mobile device, the host server, orany combination of the two.

As mentioned above, the host server may undertake further processing ofthe captured and/or preprocessed image from the mobile device, infurtherance of detecting mobile device defects that may be identifiablewithin the captured image. Processing may be completed within the serverprocessor, within a neural network coupled to the server processor, or acombination thereof. For example, the server memory may includeinstructions that when executed by the server processor cause the serverto perform the steps of: receiving the captured image from the servercommunications interface; extracting, from the captured image, asubimage corresponding to the mobile device screen; transforming theperspective of the subimage into a rectangular aspect; resampling thetransformed subimage to a predetermined input image data configuration;presenting the resampled transformed subimage to a pre-trained neuralnetwork to identify one or more device faults; and obtaining from theneural network an indication of a defect that corresponds to the screenof the mobile device.

Obtaining from the neural network an indication of a defect may furthercomprise obtaining, from the neural network, indicia showing a portionof the mobile device screen that activates a defective class in thepre-trained neural model. Such indicia may include a colored polygonthat is rendered in such a manner to circumscribe the portion of themobile device that activates the defective class. Such indicia may alsoinclude a heat map corresponding to areas most likely associated withthe defective class, with likelihood of defect correlating to anassigned color in the heat map. For instance, red in the heat mapindicates an area most likely to contain a defect, oranges and yellowsless likely, greens even further likely, light blues less likely still,and blue/black/clear least likely to correlate to an area containing adefect. The classification may occur in any desired manner. For exampleobtaining from the neural network an indication of a defect may furthercomprise identifying a local area of a fault in the display of themobile device using class activation mapping.

The neural network of the present invention may be pre-trained in anydesired manner before data is applied to the input of the network forclassification. Those of skill in the relevant arts understand that theneural network may be trained with any desired or conventional trainingmethodology such as backpropagation. For example, training thepre-trained neural network may be accomplished with training data storedin a database in the server, the training data comprising at least:device screen training images and identified defects respectivelycorresponding to the device screen training images. The identifieddefects stored in the training database correspond to mobile devicedefects identifiable by aspects of the present invention includingdefects on the display of the mobile device or in fact any other aspectof the mobile device such as its casing. These detectable defects maycomprise, for example, a crack or scratch in the display of the mobiledevice; a check, crack, chip, blemish, or break in a casing of themobile device; a bezel crack; a wear point on a casing of the mobiledevice; a stuck or dead pixel on the display of the mobile device; LCDbleed in the display of the mobile device; a pink hue defect in thedisplay of the mobile device; an object (such as a user's body part, asticker, or other item) blocking the display of the mobile device; adetected aspect ratio of the mobile device screen not corresponding toan expected aspect ratio for the corresponding mobile device; a wearpattern on the mobile device not corresponding to an expected usageprofile of the mobile device; a reflection on the display of the mobiledevice; a smudge on the display of the mobile device; a logo or otheridentifier found in the image of the mobile device not corresponding toan expected manufacturer of the mobile device; a seam separation on themobile device; a missing component on the mobile device; and any othertype of defect that may be determined from an image captured of themobile device. Any of the aforementioned defects intended to be detectedby the mobile device may be assigned to a defective class in the neuralnetwork training implementation.

Once embodiments of the present invention are presented with capturedimages of mobile devices, the analysis results may be stored in databaseto augment the training data, allowing for additional retraining of theneural network to improve its defect detection performance. Trainingdata may also be manually modified to identify defects in a capturedimage of a mobile device where the neural network previously failed todetect a defect. Further, training data may also be manually modified toidentify a captured image of a mobile device where the neural networkpreviously indicated a defect was present, but as a result of a falsepositive, no defect should have been identified. As such, the trainingdata stored in the database may be augmented in any desired manner witha new training image corresponding to a captured image of the mobiledevice and a corresponding identified defect associated with thecaptured image of the mobile device, or an area erroneously identifiedas containing a defect when in fact, no defect exists. Once trainingdata is updated in the database the pre-trained neural may be re-trainedwith the augmented training data at any desired time, or on a periodicbasis such as a determined schedule, or after a predetermined number ofaugmented images are added to the database.

The classification system may operate on any desired pixel, voxel,and/or color configuration of the image data, for example where thepredetermined input image data configuration may comprise a pixelconfiguration of 320×544 pixels. The neural network may comprise anydesired architecture, including a convolutional neural network (CNN)architecture such as described more fully below. Further the neuralnetwork may be implemented in any desired manner, such as throughsoftware stored in the server memory, through external hardwarecommunicatively coupled to the server processor, or through acombination of stored software and external hardware. One non-limitingexample of external hardware may include a dedicated neural networkprocessor, or a GPU (graphics processing unit) configured to performneural network operations.

Methods of the present invention may be executed in all or part of themobile device, the host server, external neural network hardware, andservers and/or processors communicatively coupled to the foregoing.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present invention may be derived byreferring to the detailed description and claims when considered inconnection with the following illustrative figures.

FIG. 1 illustrates a flowchart of an exemplary method of the presentinvention.

FIG. 2 depicts an exemplary display of a mobile device with instructionsand prompts to the user.

FIG. 3 depicts another exemplary display of a mobile device withinstructions and prompts to the user.

FIG. 4 illustrates one aspect of a display of an exemplary mobile deviceof the present invention that had been successfully and completelypainted

FIG. 5 shows a display of an exemplary mobile device of the presentinvention with an indication that a crack has been detected in thedisplay

FIG. 6 shows one embodiment of a display of a mobile device of thepresent invention, where the user is being prompted to end or continuethe test.

FIG. 7 illustrates a flow chart depicting a preferred embodiment of thepresent invention.

FIG. 8 illustrates a block diagram of a system embodiment of the presentinvention.

FIG. 9 shows an exemplary display of a mobile device of the presentinvention, with a prompt and a pattern for the user to follow in“painting” the display of the mobile device.

FIG. 10 shows an exemplary display of a mobile device of the presentinvention, with a partially “painted” display.

FIG. 11 shows an exemplary display of a mobile device of the presentinvention, where, in one aspect, the display had been partially painted.

FIG. 12 shows an exemplary display of a mobile device of the presentinvention; where in one aspect a prompt is presented to the user.

FIG. 13 shows an exemplary display of a mobile device of the presentinvention, wherein one aspect a prompt is presented to the user.

FIG. 14 shows an exemplary display of a mobile device of the presentinvention, wherein a prompt is presented to the user to hold still.

FIG. 15 shows an exemplary display of a mobile device of the presentinvention, wherein a prompt is presented to the user indicating thatcapture has been completed.

FIG. 16 shows a process flow diagram of the present invention,illustrating image capture and analysis to identify device defects.

FIG. 16A shows a process flow diagram of the present invention,illustrating image capture and analysis to identify device defectswithout first installing an app on a mobile device.

FIG. 17 illustrates a process flow diagram of the present invention,providing more detail of an exemplary image capture process.

FIG. 18 illustrates a process flow diagram of the present invention,providing more detail regarding an exemplary image pre-processingmethod.

FIG. 19 shows an exemplary display of a mobile device with a defect on adisplay of the mobile device.

FIG. 20 shows an exemplary image of FIG. 19 after threshold techniquesare applied.

FIG. 20A illustrates an exemplary perspective-transformed subimage of adisplay of a mobile device, corresponding to the display portion of thecaptured image in FIG. 19.

FIG. 20B illustrates an exemplary output of the present inventionidentifying a polygon-circumscribed defect of a display corresponding tothe device of FIG. 19.

FIGS. 20C, D, and E respectively show: an exemplary device image forinput to a convolutional neural network of the present invention, a heatmap output identifying a defect likelihood area corresponding to thedevice image shown in FIG. 20C, and a polygon-circumscribed defect of adisplay corresponding to the device image shown in FIG. 20C.

FIGS. 20F, G, and H respectively show: an exemplary device image forinput to a convolutional neural network of the present invention, a heatmap output identifying a defect likelihood area corresponding to thedevice image shown in FIG. 20F, and a polygon-circumscribed defect of adisplay corresponding to the device image shown in FIG. 20F.

FIG. 21 shows an exemplary display of a mobile device of the presentinvention, showing corner fiducial markers.

FIG. 22 illustrates a template-matched modification of the image of thedisplay shown in FIG. 21.

FIG. 23 illustrates template-matched corner markers obtained from thedisplay image in FIG. 21 as used to determine edges of the displayassociated with a mobile device.

FIG. 24 illustrates a block diagram with data flows depicting oneconvolutional neural network arrangement regarding systems of thepresent invention.

FIG. 25 illustrates a comprehensive flow diagram describing neuralnetwork training from network training data, and network inferenceprocesses to identify defects using the trained neural network.

DETAILED DESCRIPTION

The exemplary system depicted in FIG. 8 comprises a mobile device 800that includes a processor 810 coupled to a memory 820 which may includevolatile memory, nonvolatile memory (such as FLASH memory) or acombination thereof. A communications module 830 comprises a wirelesstransceiver 840 for wirelessly communicating with one or more servers,such as host server 860 and other entities through antenna 850, althoughthose of skill in the art may appreciate that a wired connection may beestablished to provide connectivity in lieu of or in addition to thewireless connection. The mobile device also includes a user interface870 coupled to the processor 810. The mobile device 800 may include anysuitable power source, such as a battery (not shown). The mobile device800 may include any other desired components, such as a globalpositioning system (GPS) to provide geolocation information for locatingthe mobile device. Some or all of the components of the mobile device800 may include (or be in communication with) a hardware identificationmodule (not shown) such as a universal subscriber identity module and/orremovable user identity module. The hardware identification module maybe coupled to the processor 810 and may include an identifier that canbe compared to a predetermined identifier to determine whether thehardware of the mobile device 800 has been altered. The hardwareidentification module (and predetermined identifier) may include anysuitable identifier, such as an electronic serial number, a local areaidentity identifier, an integrated circuit identifier, an internationalmobile subscriber identifier, an authentication key identifier, and/oran operator-specific emergency number identifier. The identifier may bestored in the memory 820 and transmitted to the host server 860 forcomparison to a predetermined identifier.

The functionality of the mobile device 800, including the methodsdescribed herein (in whole or in part), may be implemented through theprocessor 810 executing computer-readable instructions stored in thememory 820 of the mobile device 800. The memory 820 may store anycomputer-readable instructions and data, including softwareapplications, user-installed or third-party-installed “apps,” applets,and embedded operating code.

Additionally, the software application may be configured to operate withminimal underlying hardware functionality. For example, the applicationmay be initiated before the mobile device establishes a networkconnection. Such a situation may be provided, for instance, when thesoftware application is installed on a SIM card in the mobile device,and the application launches before other software in the mobile deviceoperating system. Alternately or in addition, a data element such as alink or a URL (universal resource locator) may reside on the SIM card,and by launching an application such as a browser with the URL or link,an application referenced by the link or URL may be loaded into themobile device from a remote server and/or executed directly from on theremote server.

Software performing methods of the present invention may be providedwith the device or downloaded onto the mobile device by an authorizeduser, and/or may be further resident in memory 16 of the host server 860and executable by the server processor 14. The functionality of themobile device 800 as well as the host server 860 may also be implementedthrough various hardware components storing machine-readableinstructions, such as application-specific integrated circuits (ASICs),field-programmable gate arrays (FPGAs) and/or complex programmable logicdevices (CPLDs), graphics processing units (GPUs), and neural networkprocessing or simulation circuits. Systems according to aspects of thepresent invention may operate in conjunction with any desiredcombination of software and/or hardware components.

The processor 810 retrieves and executes instructions stored in thememory 820 to control the operation of the mobile device 800. Similarlythe server processor 14 retrieves and executes instructions stored inthe server memory 16 to control the operation of the host server 860.Any number and type of processor such as an integrated circuitmicroprocessor, microcontroller, and/or digital signal processor (DSP),can be used in conjunction with the present invention. The memory 820stores instructions, data, messages transmitted from (or received by)the mobile device 800, and any other suitable information, and theserver memory 16 similarly stores instructions, data, messagestransmitted from (or received by) the host server 860, and any othersuitable information. A memory 820 and server memory 16 operating inconjunction with the present invention may include any combination ofdifferent memory storage devices, such as hard drives, random accessmemory (RAM), read only memory (ROM), FLASH memory, or any other type ofvolatile and/or nonvolatile memory. Data can be stored in the memory 820or server memory 16 in any desired manner.

The communications interface 830 communicates with one or more serverssuch as host server 860, or other suitable entities. In like manner, thecommunication interface 18 of the host server is configured tocommunicate with the mobile device 800, a general network such as theInternet, or any other suitable entity. Any suitable communicationsdevice, component, system, and method may be used in conjunction withthe present invention. For example, the wireless transceiver 840 may beconfigured to communicate using any number and type of cellularprotocols, such as General Packet Radio Service (GPRS), Global Systemfor Mobile Communications (GSM), Enhanced Data rates for GSM Evolution(EDGE), Personal Communication Service (PCS), Advanced Mobile PhoneSystem (AMPS), Code Division Multiple Access (CDMA), Wideband CDMA(W-CDMA), Time Division-Synchronous CDMA (TD-SCDMA), Universal MobileTelecommunications System (UMTS), and/or Time Division Multiple Access(TDMA). A mobile device operating in conjunction with the presentinvention may alternatively (or additionally) include wirelesstransceiver(s) (and related components) to communicate using any othermethod of wireless communication protocol, such as an ISO 14443protocol, an ISO 18000-6 protocol, a Bluetooth protocol, a Zigbeeprotocol, a Wibree protocol, a WiFi protocol, an IEEE 802.15 protocol,an IEEE 802.11 protocol, an IEEE 802.16 protocol, an ultra-wideband(UWB) protocol; an IrDA protocol, and combinations thereof; and further,the communication interface 18 of host server 860 may be configured tooperate with such protocols to communicate with the mobile device 800 orany other device. The antenna 850 may be configured to transmit andreceive any wireless signal in any format, and may comprise a pluralityof different antennas to transmit and receive using different wirelessprotocols.

The communications module 830 can communicate with the server 860 oranother device using any other form of connection, such as a wiredInternet connection, a wireless Internet connection, a cellulartelephone network connection (including a data link connection), awireless LAN connection, a wireless WAN connection, an opticalconnection, a Firewire connection, Thunderbolt connection, a Lighteningport connection, an e-SATA connection, a USB connection, a mobile devicesynchronization port connection, a power connection, and/or a securitycable. The communications module 830 can be used to communicate with oneor more companion devices to monitor the position or status of themobile device 800 (e.g., by monitoring whether a communication linkbetween the mobile device and companion device is intact), as well aswith any number of other devices to help track/locate a lost or stolenmobile device 800.

The mobile device 800 includes a user interface 870. The user interface870 may include any number of input devices (not shown) to receivecommands, data, and other suitable input from a user, as well as anynumber of output devices (not shown) to provide the user with data,notifications, and other suitable information from the mobile device800. Likewise, the host server 860 includes user interface 15, and mayinclude any number of input devices (not shown) to receive commands,data, and other suitable input from a user or third party, as well asany number of output devices (not shown) to provide the user/third partywith data, notifications, and other suitable information from the hostserver 860.

Any number of input devices may be included in the user interfaces 870,15 such as touch pads, touch screens, a mouse/trackball/trackpad, amicrophone, and/or an alphanumeric keypad to allow a user to enterinstructions and data into the mobile device 800 and host server 860.The term “touch screen” for purposes of the present application mayinclude a display integrated with or in close proximity to a touchinterface that is capable of determining when a user applies physicalconnection to a location proximate the display. The touch screen mayhave sensors that can measure parameters from the user's interaction,and such sensors may measure capacitance, resistance, pressure, ordifferential readings resulting from movement of a “touch” to thescreen. The user interface 870 may be configured to detect pressureexerted by a user on the keys of a keypad (virtually implemented on thedisplay, or as a physical array of key switches), as well as the timeinterval between key presses in order to determine if the current useris authorized to use the device. The user interface 870 may also includea microphone to allow the user to provide audio data to the mobiledevice 800, as well one or more cameras to allow the mobile device tocapture still or video images. Similarly, the user interface 15 of thehost server 860 may include a microphone to allow a user to provideaudio data to the host server 860, as well one or more cameras to allowthe server 860 to capture still or video images. In one embodiment, themobile device 800 comprises a front-facing camera 874 that faces theuser when the device is in operation, and a rear-facing camera 872 on anopposite side of the mobile device. The mobile device 800 may includespeech recognition software to process verbal input through the userinterface 870. The user interface 870, and similarly the server userinterface 15 may also include any number of suitable output devices,such as a display screen to visually display information (such as videoand text), and/or a speaker to provide auditory output. The display ofthe mobile device may be configured to sense user touches by anyappropriate means, such as capacitive sensing, pressure sensing, geldisplacement sensing, resistive sensing, or any other appropriate orconventional touch sending technology utilized by those of skill in therelevant arts. The mobile device 800 may be configured to provide words,phrases, tones, recorded music, or any other type of auditory output toa user through the speaker. As discussed previously, the user interface870 can be activated to provide information and/or hinder the operationof the mobile device 800 when an unauthorized user attempts to use themobile device 800. For example, the illumination level of the displaymay be modulated to draw attention to the mobile device, and unpleasantand/or loud sounds can be played over the speaker.

The mobile device 800 may include one or more biometric devicesconfigured to receive biometric information, such as a fingerprintscanner, an iris scanner, a retinal scanner, and/or a breath analyzer.Input devices such as a microphone or camera may also be utilized toperform biometric analyses, such as a voice analysis or facialrecognition. Further, the mobile device may include a magnetometer formeasuring magnetic fields (such as may be utilized in an electroniccompass), a MEMS or other type of gyroscope for measuring attitude, andaccelerometers for measuring changes in movement of the mobile device.

Information provided or received by the user interfaces 870, 15 may bein any appropriate format. For example, a user interface thatcommunicates information to a user in an auditory format may firstprovide a data header followed by a data value to identify the data tothe user. The user interfaces 870, 15 may provide information in anynumber of desired languages, regardless of whether the information isprovided audibly or visually.

The user interfaces 870, 15 can also provide/receive information to auser in a machine-readable format. In one exemplary embodiment of thepresent invention, for example, the user interface 870 of a mobiledevice 800 may send and receive messages using dual-tone multi-frequency(DTMF) tones. The mobile device 800 and host server 860 can beconfigured to send, receive, and process machine-readable data in anystandard format (such as a MS Word document, Adobe PDF file, ASCII textfile, JPEG, or other standard format) as well as any proprietary format.Machine-readable data to or from the user interfaces 830, 15 may also beencrypted to protect the data from unintended recipients and/or improperuse. In an alternate embodiment, a user must enter a passcode to enableuse of some or all of the functionality of the mobile device 800. Anyother user interface feature may be utilized to allow a human ornon-human user to interact with one or more devices operating inconjunction with the present invention.

The mobile device 800 may include any other suitable features,components, and/or systems. For example, the mobile device 800 may beconfigured to preserve the life of its battery by shutting off some orall of its components, such as a camera or microphone. Components can beselectively shut down in response to a security compromise event, aswell as in response to a command from an authorized user or securityauthority. Alternately, the mobile device 800 can be configured to useits components excessively to drain the battery as quickly as possible,to, for example, limit the usefulness of the mobile device 800 to anunauthorized user.

The mobile device 800 may be configured to implement one or moresecurity measures to protect data, restrict access, or provide any otherdesired security feature. For example, a mobile device 800 may encrypttransmitted data and/or data stored within or created by the deviceitself. Such security measures may be implemented using hardware,software, or a combination thereof. Any method of data encryption orprotection may be utilized in conjunction with the present invention,such as public/private keyed encryption systems, data scramblingmethods, hardware and software firewalls, tamper-resistant ortamper-responsive memory storage devices or any other method ortechnique for protecting data. Similarly, passwords, biometrics, accesscards or other hardware, or any other system, device, and/or method maybe employed to restrict access to any device operating in conjunctionwith the present invention.

The host server 860 communicates with mobile devices 800, authorizedusers, unauthorized users, security authorities, (insurance agencies inparticular) and other entities to monitor and protect the mobile devices800 from unauthorized use and to mitigate the harm associated with asecurity compromise event or attempted fraud. The host server 860 maycomprise any number of separate computer systems, processors, and memorystorage devices, as well as human operators (e.g., to answer calls fromauthorized users reporting the loss/theft of a mobile device) and anyother suitable entity. The host server 860 may include, or be incommunication with, one or more databases 880 storing informationregarding authorized users and mobile devices 800 in order to monitorand track the mobile devices 800 and provide instructions to the mobiledevices 800 in the event a security compromise event occurs.

For example, a database 880 may store a usage profile for a mobiledevice to allow software on the host server 860 to detect whethercontinued usage of the mobile device deviates from the usage profile bya predetermined threshold, or whether the mobile device has incurred aloss event resulting in a fault state within the mobile device 800. Thehost server 860 may also receive, process, and store (e.g., in thedatabase 880) information from the mobile device 800. The host server860 may handle any type of data in any format to achieve any purpose,such as receiving and processing environmental parameters captured bythe mobile device to track the position and location of the mobiledevice 800 as discussed previously. The database 880 may also storelocation information that can be used to determine whether the mobiledevice 800 is operating in a valid location (e.g., “whitelisting” and“blacklisting” as discussed previously). The database 880 may also storeneural network training data, wherein exemplary images of defects inmobile devices are correspondingly associated with identified defectclasses, outputs or states. The neural network training data may beaugmented or otherwise modified to improve accuracy of recognition ofdevice defects. Further, database 880 may store results of analysis ofthe health and operation of mobile device 800, along with identifyinginformation and historical operational information associate with mobiledevice 800. In this manner, operational analysis of the mobile devicemay be tracked and analyzed over time by server 860, and trendsidentified that may indicate pending failure of at least one componentof mobile device 800.

Databases 880 in communication with the host server 860 may also storearchived data from mobile devices 800 for recovery in the event themobile devices 800 are lost or stolen, or the data on the mobile devices800 is destroyed (e.g., by a virus or other malicious program). Thefunctionality of the host server 860 may be performed automatically orsemi-automatically, such as through software/hardware operating on oneor more computer systems, and/or by one or more human operators.

The host server 860 may include one or more system processors 14 thatretrieve and execute computer-readable instructions stored in a memory16 to control (at least partially) the operation of the host server 860.Any number and type of conventional computer, computer system, computernetwork, computer workstation, minicomputer, mainframe computer, orcomputer processor, such as an integrated circuit microprocessor ormicrocontroller, can be used in conjunction with the present invention.Computer systems used in accordance with aspects of the presentinvention may include an operating system 43 (e.g., WindowsNT/95/98/2000/XP/Vista/7/8/10, OS2, UNIX, Linux, Solaris, MacOS, etc.)as well as various conventional support software and drivers typicallyassociated with computers. In certain embodiments, dedicatedapplications may be entirely or partially served or executed by thesystem processor to perform methods of the present invention.

The neural network 47 may comprise a convolutional neural network (CNN)architecture, and may be implemented through software stored in thememory device 16, through external hardware (such as hardwareaccelerators or graphics processing units (GPUs)) coupled to the serverprocessor 14, or through a combination of stored software and externalhardware.

The host server 860 may be accessed in any desired manner, such asthrough a website on the Internet, and/or through a telephone network.The host server 860 may include any number of human operators, computersystems, mobile telephones, mobile computing devices, interactive voiceresponse (IVR) systems, and any other suitable system and device forcommunicating with a user, security authority, computing device, orother entity. The host server 860 can communicate with unauthorizedusers of a lost or stolen mobile device, both through the mobile deviceor through other communication methods, including direct communicationwith a fault state test application (alternatively, as used herein,“FSTA”) installed on the mobile device. The host server 860 may notifythe unauthorized user that the mobile device is lost or stolen, providerecovery information (such as a shipping address) to the unauthorizeduser, forward test result and damage claim information to a third partyinsurer, and facilitate initiation of an insurance claim. The hostserver 860 also communicates with the mobile device 800 to providesoftware updates, receive data for archival, identify files and otherdata to be protected, and to perform any other aspect of the presentinvention.

The host server 860 may be controlled by (locally or remotely), oroperate in conjunction with any third party such as an authorized user,telecommunications service provider, mobile device monitoring/trackingservice provider, security authority, an insurance agency, a mobiledevice repair agency, a mobile device seller, an advertising agent, amobile device warranty reseller, an authorized user of the mobiledevice, and/or any other desired entity. For example, authorized usersand security authorities may communicate with or through the host server860 to interact with the fault state test application (FSTA) installedon mobile device 800 to confirm that the device has incurred a faultthat may be subject to an insurance claim. The host server 860 may beconfigured to provide notifications on how to return a lost/stolenmobile device 800, detect a security compromise event, detect that afault state has arisen on the mobile device, and determine whether amobile device's functionality should be altered and (if so) determinethe manner in which the functionality of the mobile device 800 should bealtered. The host server 860 may operate in conjunction with any otherdesired systems, devices, human operators, or other entities.

The FSTA may gather information from the tested mobile device to berelayed to the third party or stored 45 in memory 16 of the server 860along with the results of the device test. Device data 45 is uniquelyassociated with one or more images of the mobile device 46, which may beanalyzed by the server processor 14. Device image data may be stored inany format, including, but not limited to jpeg, TIFF, GIF, EPS, PNG,PDF, scalable vector graphics, bitmaps, or any other conventional ornonconventional image data formats. The device data 45 and correspondingdevice images 46 may also be stored in the database 880. Any appropriatedata may be included in the information relayed to the third party, suchas a device type, a manufacturer, a model number, a serial number, amanufacturing date, a hardware configuration list, a memory capacity, asoftware manifest, a list of operable features, a list of inoperablefeatures, an electronic serial number, an ESN, an IMEI number, aninternational mobile equipment identifier number, an IMSI number, aninternational mobile subscriber identity number, a UIMID number, and auser identity module identifier, test results, and fault conditions.Mobile devices that utilize SIM cards (such as interchangeableSubscriber Identity Modules commonly used with cellular telephones) canbe further used with embodiments of the present invention in determiningthat at least one of the stored device configuration parameters includesan IMSI number within a SIM of the device.

In an additional embodiment of the present invention, a user that has amobile device with an alleged fault condition (such as a crackedscreen/display) may be requested to install an application (such as theFault State Test Application referred to above) on the allegedly faultymobile device. The FSTA provides a trusted method to assess the veracityof a claimed fault condition on the mobile device (such as a cracked ordamaged screen/display). The FSTA interacts with the mobile device'suser to obtain sensor readings and user inputs that are utilized toassess whether the mobile device is in fact faulty. The determination ofa fault/non-fault condition can then be displayed and/or sent to a thirdparty (such as an insurance company that is being asked to providepolicy coverage for the mobile device) in a secure manner (so that thetest performed on the mobile device provides a trusted result). In oneembodiment, the data sent by the FSTA in the mobile device is encryptedto prevent tampering or spoofing by the user of the mobile device, andis suitably decrypted by the recipient or software running within theserver 860.

Embodiments of the present invention that test the mobile device alsohave wider purposes beyond testing mobile devices for fractured glass;for example: full digitizer could be tested as the screen is painted infull (as described more completely below). Further, a darkeneddisplay/screen could be tested by a point test (e.g. sensors might beworking but the LCD could be broken). Likewise, to verify that the inputfrom the user is accurate, in one implementation the user would not beable to see points to complete test whereas in normal paint mode, theuser might try to paint full screen to fool test. Also, color could betested; for instance, the user could be prompted to press the 2displayed greens then press the two reds and finally press the 2 blues(for example). Further, analysis of an image captured of the mobiledevice, either in a mirror or by a second device, may provide capturedimage data that can be analyzed to visually determine fault classes,such as through a neural network such as a convolutional neural network.A wide variety of defects may be detected in this manner, including, forexample, a crack or scratch in the display of the mobile device; acheck, crack, chip, blemish, or break in a casing of the mobile device;a bezel crack; a wear point on a casing of the mobile device; a stuck ordead pixel on the display of the mobile device; LCD bleed in the displayof the mobile device; a pink hue defect in the display of the mobiledevice; an object (such as a user's body part, a sticker, or other item)blocking the display of the mobile device; a detected aspect ratio ofthe mobile device screen not corresponding to an expected aspect ratiofor the corresponding mobile device type; a wear pattern on the mobiledevice not corresponding to an expected usage profile of the mobiledevice; a reflection on the display of the mobile device; a smudge onthe display of the mobile device; a logo or other identifier found inthe image of the mobile device not corresponding to an expectedmanufacturer of the mobile device; a seam separation on the mobiledevice; a missing component on the mobile device; and any other type ofdefect that may be determined from an image captured of the mobiledevice.

Further, in one embodiment, a company that sells mobile devices may wantto know immediately if a device in the field has incurred a faultcondition (such as a cracked display) so that a new model can be offeredto the device owner. This may be especially relevant in the case where aMobile Network Operator (MNO) offers devices for sale that are subsidedby the payment of the service plan's monthly fees over acontractually-obligated period of time—the MNO benefits because they canrenew and extend the service agreement, and the device manufacturerbenefits because they can sell new devices to replace faulty ones.Additional or alternate system implementations of embodiments of thepresent invention include:

-   -   Mobile Device Insurance: embodiments of the present invention        provide for verification of phone condition in the second-hand        mobile device insurance market, as well as providing the        capability to support selective insurance or variable policy        provisions related to provable state/functionality of the mobile        device hardware (e.g. screen not cracked results in full        insurance for mobile device, but a cracked screen but otherwise        functional device may allow for a policy to cover the device        except for glass damage). Likewise, embodiments of the present        invention provide for verification that the phone being checked        is the one being insured (e.g. through transmission of mobile        device ID information such as IMEI information or other data        described herein as part of the test). Further, embodiments of        the present invention may be used in identifying that the mobile        device is the correct device under warranty before it is        returned for repair of screen to reduce spurious claims. Such        may occur, for example, for an insured device, when a repair        company doesn't really repair a broken device, but instead        fraudulently splits insurance payout with a person whose phone        was insured.    -   Mobile Device Transfer: Embodiments of the present invention may        be utilized to validate that the phone that was tested is the        one actually handed over in a transfer of possession/ownership.    -   Lease Return State Verification: aspects of the present        invention provide for determination of condition for lease        return (for example, if a leased mobile device must be returned        at end of lease, if the condition of the mobile device has        fallen below a predetermined agreed-upon state (i.e. display        must not be cracked at time of lease return) then the customer        must pay an additional fee for the excessive wear/damage).    -   Purchase/Shipping State Verification: The FSTA could be used for        verification by a consumer buying the device online (for        example, through eBay) where the seller is not completely        trusted. Also, the FSTA could be used to ascertain a current        level of damage i.e. the display may be cracked but to what        extent (in various embodiments it is anticipated that a consumer        or company would pay additional money as the know screen        condition is not too badly damaged as to become unacceptable).        Additionally, embodiments of the present invention provide proof        by a warehouse that the mobile device was in acceptable        condition when it was dispatched. When the FSTA is used a        verifier in the situation where a person is purchasing a used        mobile device from another source, the buyer may request the        seller install a trusted screen verifier app, put the buyer's        email address or SMS number in the FSTA to provide confirmation,        the FSTA runs the test and sends the results to the buyer, thus        verifying the used device does not have known faults (such as a        cracked screen). Further this might be desired by the seller,        who wants to verify before shipping, that the device is        operating as expected, so that they are not accused of sending a        faulty device to the recipient.    -   Legal Status Determination: Aspects of the present invention may        support collaboration with third parties (or a police database)        in confirming that the mobile device is not lost/stolen/subject        to outstanding charges.

There are several embodiments provided for herein to perform the faultstate assessment, including the FSTA conducting a pressure sensor-basedtest, a magnetometer-based test, or a pixel-based test, along withseveral system-based use scenarios presented; while each type of FSTAtest is intended to be able to determine a crack individually, thevarious types of crack detection approaches may be combined as desiredfor any purpose, such as increased accuracy. While in a preferredembodiment, the FSTA may be installed in and run resident within themobile device, those of skill in the art may appreciate that the FSTAmay be executed in other manners, such as an OTA (over the air)application; deployed through a website and side loaded to a connectedmobile device; loaded through a piece of in-store hardware (e.g.raspberry pie) or purpose-built instore device; or loaded and executedfrom a mall or store vending machine. Further, all or part of the FSTAand system implementation embodiments may be included in a library ofapps utilized by entities that provide online quotes for insurance,device trade in, or mobile device diagnostics.

Pressure Sensor Based Test:

In mobile devices equipped with pressure sensors, the FSTA is configuredto obtain pressure sensor readings from touches to the display of themobile device. By prompting the user to touch certain areas of thescreen, simultaneously touch multiple areas of the screen, or dragtouches across multiple areas of the screen, (or any other desiredcombination) the FSTA can obtain a collection of pressure measurementsfrom the touches and determine whether the display/screen is cracked.While typically a user's finger press causes a “touch” to occur, thoseof skill in the art appreciate that in embodiments of the presentinvention, any body part or mechanical stylus may be used in addition toor in replacement for a finger when executing a “touch” to the display.

In one preferred embodiment shown in FIG. 1, the user executes 110 theFSTA app (after installation 105, if the app had not already beeninstalled), and the user is prompted 115 by the FSTA to touch twofingers to the display, either left finger first then right finger, orright finger first then left finger, or both approximatelysimultaneously, as may be desired in obtaining the most appropriatepressure readings. The FSTA detects the pressure readings for each touchzone, and the test continues by prompting 120 the user to drag the twofingers across the screen to “paint” the screen, as FSTA displaysillumination 125 on the display when it has detected touches ofsufficient measured pressure in the areas. The user may be prompted tosimply drag both fingers in any direction until the entire screen is“painted” by the FSTA app (after it detects all areas of the screen havereceived the appropriate drag touches) or preferably, to follow aparticular path in “painting” the screen with the two finger touches.FIG. 2 shows an exemplary display 200, with a prompt 205, and a pattern210 for the user to follow in “painting” the display 200. While aserpentine pattern is shown, alternative patterns may be used dependingon the mobile device type, the pressure sensor configuration, or otherfactors. FIG. 3 shows a partially “painted” display 200 with the pattern210 for the finger touches, and color 310 filling the display 200 wherethe two finger touches 315 had followed the pattern 210. FIG. 4 showsone aspect of a display 200 that had been successfully and completelypainted, with all areas of the pattern prompt 210 covered. Whileembodiments shown herein show a completely painted display as an exitcriterion, various embodiments anticipate the possibility of paintingjust a portion of the display, painting no parts of the display, orhalting the test as soon as a fault condition (e.g. a crack) is detectedwithout painting or covering the entire display.

Alternatively or in combination, the FSTA may present the prompts in agame-like manner, prompting the user, for instance to drag the fingersto move one displayed object toward another (for example: moving animage of a rabbit toward a carrot, or helping someone through a mazelike a kids puzzle, or pressing and sliding the blocks into the rightorder to complete a picture), or to “capture” an item moved across thedisplay, thus prompting the user to touch the desired sections of thescreen. Alternatively, or in combination, more than two fingers may beused simultaneously on the display, such as three or four fingers placedon the display significantly spaced apart, then dragged together towarda prompted location.

The FSTA monitors the progress of the user in completing the test, andif necessary, prompts the user 130 to complete or end the test. Once thetest is complete, the FSTA analyzes the accumulated pressure data toattempt to determine anomalies such as touch pressure reading changesthat indicate that predetermined thresholds have been met fordetermining that a crack may be present in the display 200. In oneembodiment, when a pressure reading of one of the touched areas fallsoff by more than a predetermined threshold, a display anomaly such as acrack may be determined to be present. In various embodiments, thepredetermined fall-off thresholds may comprise 5%, 10%, or 50%). Inother embodiments, when pressure from one finger touch falls to lessthan a predetermined amount of its previous value, a discontinuity inthe display is likely (in one example, the predetermined amount may be10% or 50% of its average reading). In yet another embodiment, thedifference in pressure readings between finger touches is compared overtime, and if the difference between the pressure readings varies morethan a predetermined difference threshold, a crack may be determined tobe present within the display. In yet another embodiment, a drop thenrise in pressure readings from at least one of the finger touches mayindicate the presence of a crack in the display. In a furtherembodiment, a rise then drop in pressure readings from at least one ofthe finger touches may indicate the presence of a crack in the display.

After analysis is complete, the appropriate test result is displayed 135and/or transmitted (optionally, along with mobile device ID informationand accumulated test data) to a central server 860 and/or to a thirdparty, such as an insurance agency, a mobile network operator, a mobiledevice manufacturer, or to any entity for which fault states of themobile device are of importance. In one embodiment, FIG. 5 shows anindication 510 that a crack has been detected in the display. FIG. 6shows one embodiment, where the user is being prompted 610 to end orcontinue the test.

As mentioned above, while the user is making the prompted movements asdescribed, the FSTA accumulates and stores pressure data for each of thetouched (or swiped/dragged) zones over time. In doing so, the FSTA canuse predetermined methods to analyze anomalies in the accumulatedpressure measurements that may indicate the presence of one or more acracks in the display of the mobile device. For example, in the case ofmobile devices with pressure-sensitive displays (such as certain iPhonemodels), the FSTA may record and identify significant pressure drop-offanomalies when the finger pad passes a discontinuity in the screen (suchas a crack). The FSTA may then take the appropriate action to reportstatus to the user, and alternatively to the outside entity as mentionedabove.

Magnetometer-Based Test.

In another embodiment, the mobile device 800 includes a magnetometerthat may be accessed by the FSTA to accumulate magnetometer readings,and in conjunction with prompted touches by the user of the mobiledevice, the FSTA may detect fault conditions such as screen cracks thatarise from pressure changes sensed by the magnetometer when the userpresses certain areas of the screen. While typically a user's fingerpress causes a “touch” to occur, those of skill in the art appreciatethat in embodiments of the present invention, any body part ormechanical stylus may be used in addition to or in replacement for afinger when executing a “touch” to the display.

In one preferred embodiment shown in FIG. 7, the user executes 710 theFSTA app (after installation 705, if the app had not already beeninstalled), and the user is prompted 715 by the FSTA to touch (see alsoFIG. 9, 910) the display 200, with a pattern presented to the user (seealso FIG. 9, 915) that will need to be filled in from discrete fingertouches, as prompted 720 by the FSTA. In this embodiment, the FSTA willmeasure and accumulate magnetometer readings as each prompted area 915is pressed by the user, and if sufficient pressure is applied, eachindividual area of the display 200 is “painted” 725 to provide userfeedback (see FIG. 10, showing partially painted section 1010, andunpainted part of pattern 1015). While painting the display, the FSTAdetects and accumulates the magnetometer readings for each touched zone725, and the test proceeds until a predetermined amount of the display200 has been painted. In various embodiments, substantially all of thescreen will be required to be “painted”; in alternate embodiments, asubset of the screen area will need to be painted, and in an additionalembodiment, the test only continues until the magnetometer readingsindicate that a crack has been detected.

FIG. 9 shows an exemplary display 200, with a prompt 905, and a pattern910 for the user to follow in “painting” the display 200. FIG. 10 showsa partially “painted” display 200 with the pattern 910 denoting theremaining finger touch areas to be completed, and color 1005 filling thedisplay 200 where finger touches had been registered with sufficientpressure. FIG. 11 shows one aspect of a display 200 that had beenpartially painted 1105, with a prompt 1115 asking the user whether theyhave completed painting the defined grid area 910. FIG. 12 shows that ifthe user selected the option that they had completed the painting, andif the defined 910 had not been sufficiently completed to paint thenecessary areas (partially shown in 1205), then a prompt 1215 ispresented allowing the user to start over or to finish touching thedefined grid areas. FIG. 13 then shows one embodiment of informationpresented 1315 on the display 200, indicating that a crack has beendetected.

While embodiments shown herein show a completely painted display as anexit criterion, various embodiments utilizing the magnetometer readingapproach anticipate the possibility of painting just a portion of thedisplay, painting no parts of the display, or halting the test as soonas a fault condition (e.g. a crack) is detected without painting orcovering the entire display. Also, in various embodiments herein, once acrack condition is detected, the mobile device may be configured toprompt the user to take a photograph of the device in a mirror (with itsown forward-facing camera) to document the state of a crack in themobile devices' display.

Alternatively or in combination, the FSTA may present the prompts in agame-like manner, prompting the user, for instance, to press varioussections of the screen to achieve a game objective, or to “wall in” anitem moved across the display, thus prompting the user to touch thedesired sections of the screen. Alternatively, the magnetometer readingembodiments may be used in combination with the pressure sensorapproaches described above to improve accuracy or shorten the durationof the test to determine whether a crack is present in the display.

Returning to FIG. 7, the FSTA monitors the progress of the user incompleting the test, and if necessary, prompts the user 730 to completeor end the test. Once the test is complete, the analysis 735 of themagnetometer data is conducted to determine whether a crack is likelypresent in the display, and an appropriate test result is displayed 735and/or transmitted to a central server 860 and/or to a third party, suchas an insurance agency, a mobile network operator, a mobile devicemanufacturer, or to any entity for which fault states of the mobiledevice are of importance.

While the user is making the prompted presses as described above, theFSTA accumulates and stores magnetometer data for each of the touchedzones in the defined touch areas (such as the grid 910) over time. Indoing so, the FSTA can use predetermined methods to analyze anomalies inthe accumulated magnetometer measurements that may indicate the presenceof one or more a cracks in the display of the mobile device. Forexample, a deviation or change in magnetometer readings of more than apredefined percentage (for example, 5%, 10%, or 50%) between two or moreaccumulated magnetometer readings may indicate a crack is present in thedisplay. The FSTA may then take the appropriate action to report statusto the user, and alternatively to the outside entity as mentioned above.

Pixel Test.

In one embodiment, a user interactively verifies that a crack exists ina screen of the mobile device by interacting with an application in themobile device to indicate when one or more pixels are not illuminatingcorrectly. To verify that the user is providing correct information, theapplication may provide information that requires negative responses aswell as positive responses in order to correctly assess the state of thedevice. For example, in one embodiment, the screen could be partitionedor tessellated into regions, and the application could attempt toilluminate each screen section (with varying colors, if desired), andask the user to press any sections that do not show evenly lit pixelswithin each respective section. The areas indicated by the user ashaving unevenly lit pixels could then be tessellated into smallerregions, and the interaction repeated until a suspect region isidentified to a sufficiently small size. The application can thenprovide prompt such as “Please watch for a green pixel being displayed,”and then asking the user to click when the user saw it. If no pressoccurs after a predetermined period of time, then the area is assumed tobe nonfunctional. Further, to determine the veracity of the user'sinputs, a pixel can be presented to a “known good” area of the screen(one that the user had previously indicated during the tessellation testwas active and functional) and if the user does not press the responsepad that they affirmatively saw the pixel, then the user's inputs can bejudged to be suspect. This process may be used iteratively determine notonly the veracity of the user's inputs, but could be used to map out tohigh accuracy the extent of any nonfunctional areas of the screen. Thisembodiment makes this possible by allowing the “edge” of known goodareas to be determined where the user provides a response as seeing theilluminated pixels, and areas where no illumination was seen. Additionalpixel verifications can be conducted to determine whether any faultyareas “move” with additional pixel tests with random (or slightlydisplaced) locations on the screen, thus assessing the veracity of theuser's claim that an area of the display is faulty (e.g., cracks don't“move,” although they may grow larger).

Thus, cracked areas, stuck or dead pixels, or faulty display interfacescan be mapped out and used to assess costs of mobile devicerepair/replacement, or to determine the applicability of an insuranceclaim, or for any other desired purpose. In one additional embodiment, asecond mobile device is utilized with an FSTA installed upon it thatsynchronizes with the FSTA installed on the first suspect mobile device(such as through a Bluetooth pairing). In this scenario, the secondmobile device's camera is used to photograph or video the display of thesuspect mobile device to assess whether pixels (and/or regions) that areattempted to be displayed on the suspect mobile device are or are notvisible; as such, a full test of the screen can be automated and a rapiddetermination made as to the areas of the screen/display that are or arenot faulty without relying upon a user to provide the necessary (andaccurate) inputs.

Detection of Device Defects Through Neural Network

Embodiments of the present application utilize Convolutional NeuralNetwork (CNN) architectures to analyze captured images, videos, and/oraudio files from mobile devices to identify defects and to provideanalytical information such as heat maps in assisting with the furtheranalysis of defect types. Those of skill in the relevant arts understandthat many different CNN architectures may be used to implement aspectsof the present invention. One exemplary CNN architecture may be realizedthrough an Inception model implemented with Tensorflow™, and explanatoryexamples are provided, for instance, athttps://www.tensorflow.org/tutorials/image_recognition, the disclosureof which is hereby incorporated by reference herein for all purposes.Alternative implementations of the present invention may be implementedthrough a Deep Residual Network (an explanation available athttps://blog.waya.ai/deep-residual-learning-9610bb62c355 is incorporatedby reference herein for all purposes), and or through ConvolutionalNetwork architecture implemented in environments such as Torch (examplesand explanatory text provided at http://torch.ch/docs/tutorials.html,the disclosures of which are fully incorporated by reference herein forall purposes). Background introductions for neural network architecturesin general, and Convolutional Neural Networks (CNNs) in particular arealso provided in Michael A. Nielsen, “Neural Networks and DeepLearning”, Determination Press, 2015 available athttp://neuralnetworksanddeeplearning.com, the disclosure of which isfully incorporated by reference herein for all purposes.

In general, CNNs are one type of model architecture that has beensuccessfully used for image classification tasks. CNNs apply a series offilters to the raw pixel data of an image to extract and learnhigher-level features, which the model can then use for classification.CNNs typically contain three components: (1) Convolutional layers, (2)Pooling layers, and (3) Dense/fully connected layers.

Convolutional layers apply a specified number of convolution filters tothe image. For each identified subregion within the image, theconvolutional layer performs a set of mathematical operations to producea single value in the output feature map. Convolutional layers thentypically apply a ReLU activation function (a Rectified Linear Unit), tothe output to introduce nonlinearities into the model; however, invarious embodiments logistic sigmoid functions or hyperbolic tangentactivation functions may also be utilized.

Pooling layers down-sample the image data extracted by the convolutionallayers to reduce the dimensionality of the feature map in order todecrease processing time. Practically, a pooling function replaces theoutput of the net at a certain location with a summary statistic of thenearby outputs. A commonly used pooling algorithm is max pooling, whichextracts subregions of the feature map, keeps their maximum value, anddiscards all other values, thus reporting the maximum output within arectangular neighborhood. Other possible pooling functions include theaverage of a rectangular neighborhood, the L2 norm of a rectangularneighborhood, or a weighted average based on the distance from thecentral pixel.

Dense (fully connected) layers perform classification on the featuresextracted by the convolutional layers and the down-sampled by thepooling layers. In a dense layer, every node in the layer is connectedto every node in the preceding layer.

As introduced above, a CNN is composed of a stack of convolutionalmodules that perform feature extraction, and each module consists of aconvolutional layer followed by a pooling layer; the last convolutionalmodule is followed by one or more dense layers that performclassification. The final dense layer in a CNN contains a single nodefor each target class in the model (all the possible classes the modelmay predict), with an activation function for each node. The CNN allowsinterpretation of the values for a given image as relative measurementsof how likely it is that the image falls into each target class. Invarious embodiments of the present invention, a target class maycomprise a defect in a mobile device such as a crack in a display, ablemish or crack on a casing, LCD bleed on the display, pink hue on thedisplay, stuck pixels on the display, and various combinations of theabove.

In one embodiment using a neural network analysis approach, systems anda methods are provided to identify a crack or other kind of defectassociated with a mobile device through image analysis. Referring to theprocess 1600 illustrated in FIG. 16, an FSTA is installed 1610 on themobile device that is desired to be tested 800 (also shown in FIGS. 14and 15 with user prompts and guidance for capturing an image of themobile device in a reflective surface such as a mirror). When ready, theuser of the mobile device 800 initiates the device defect test 1615.Through a process discussed in regards to FIG. 17, an image of thedisplay of the mobile device 800 is captured 1620, and the capturedimage is optionally pre-processed 1625 by rotating the image to aportrait mode and/or fine rotating to reduce skew, and/or cropping theimage to a desired aspect ratio. The captured (and optionallypre-processed) image is then uploaded 1630 to a server (such as server860), and as discussed further in regards to FIG. 18, the uploaded imageis processed 1635 to determine whether any cracks or other types defectsare present. Alternatively, or in combination, the extent andcharacteristics of the screen cracks or defects are characterized by theprocessing step 1635 for further analysis and reporting. Once theanalysis 1635 of any potential defects is complete, the resultinginformation is formatted 1640 for presentation, storage, or display toany user for any necessary purpose, such as for assessment of whetherthe mobile device 800 is in proper working condition. The process thenexits 1645.

FIG. 17 depicts a process 1700 of the present invention for capturing animage of the mobile device, as introduced in FIG. 16 step 1620. Theimage capture process begins 1705, where the FSTA analyzes ambientlighting 1707 from a light sensor within the mobile device 800. Then1709, screen brightness and camera sensitivity of the mobile device 800are adjusted to allow the mobile device's front-facing camera (e.g.,FIG. 14, 874) to optimally follow a tracking image displayed on themobile device's display (e.g., FIG. 14, 1400), the user is directed tomove the device 800 into position 1715 with the display of the mobiledevice facing a reflective device such as a mirror. The trackingalgorithm of the FSTA identifies the mobile device and provides guidanceto the user from the display of the mobile device 800 to move/rotate themobile device 1720 until it is determined that the mobile device is inoptimal position to capture an image. As part of this process, the usermay be directed to rotate, tip, move, or align the mobile device withrespect to the reflective device's surface. The FSTA uses data obtainedfrom the device's front-facing camera (e.g., FIG. 14, 874) to determine1725 when the mobile device is in acceptable alignment for imagecapture, and then the FSTA modifies sensitivity 1730 of theforward-facing camera for optimal image capture. In one embodiment,image capture sensitivity may be different an initial sensitivity usedduring tracking of placement of the mobile device in front of thereflective surface. Depending on the operating system of the mobiledevice 800, different parameters may be modified in the mobile device toadjust camera sensitivity. In one embodiment, sensitivity is adjustedthrough changing an exposure compensation value associated with a camerain the mobile device 800. In another embodiment, shutter speed and ISOvalues associated with a camera in the mobile device 800 may be adjustedto achieve a desired sensitivity. Then, a capture image, such as a whitescreen or a screen with predetermined fiducial marks may be output 1735to the display of the mobile device 800. An image is then captured bythe front-facing camera (e.g., FIG. 14, 874) of the mobile device 800.

Those of skill in the relevant arts also appreciate that the rear-facingcamera 872 of the mobile device 800 of the present invention may be usedwhen facing a reflective surface to record an image of another surfaceof the mobile device 800, including, for instance, a back side of themobile device casing. In this manner, defects in the casing may beanalyzed by the optical neural network analysis of the present inventionto detect casing defects, wear patterns, chipping, cracking, and thelike.

Returning to FIG. 16, after the captured image is pre-processed 1625,and uploaded 1630 to the host server, image analysis may begin 1635 inthe server 860. FIG. 18 depicts an embodiment of image analysis inaccordance with the present invention, where such analysis may becarried out by the server 860. Once the captured image has been received1810 by the server 860, the captured image may be optionally downsizedor resampled to reduce the size of the image file and increaseprocessing efficiency. An example captured image may be seen in FIG. 19,wherein the mobile device 800 shows its display 1400 and is beinghandheld by a user. The edges of the display 1400 of the mobile device800 are then located in the captured image 1815 through alternativemethods.

In a first method to finding the edges corresponding to a display 1400,image threshold techniques are applied to the captured image (an exampleshown in FIG. 19) to produce a threshold-modified image (an example ofthe image of FIG. 19 after threshold techniques are applied may be seenin FIG. 20). Optical thresholding techniques are well understood tothose of skill in the relevant arts, and example tutorials are providedat https://docs.opencv.org/3.3.1/d7/d4d/tutorial_py_thresholding.html,the disclosure of which is fully incorporated by reference herein forall purposes. Next, contours are found in the thresholded image, wherecontours comprise defined curves joining all the continuous points(along the boundary), having same color or intensity in an image. Ascontours are a useful tool for shape analysis and object detection andrecognition, they may be used to assist in identification of the area ofthe captured image corresponding to the mobile device's display 1400.Contour analysis techniques are well understood to those of skill in therelevant arts, and example tutorials are provided athttps://docs.opencv.org/3.3.1/d4/d73/tutorial_py_contours_begin.html,the disclosure of which is fully incorporated by reference herein forall purposes. Once contours are determined in the thresholded images,contours having four points are analyzed and compared to known aspectratios of the mobile device under analysis. Since the device data 45identifies the device type, its display aspect ratio may be identifiedfrom preexisting lookup data (or externally from the devicemanufacturer) and may be used for comparison of aspect ratios of thefour-point contours found in the thresholded captured image. Once acontour is identified in the thresholded image that is sufficientlysimilar in aspect ratio to the defined display aspect ratio for theparticular mobile device under analysis, (for instance, the aspect ratiois within a predetermined tolerance value of the manufacturer's aspectratio data), then the location of the vertices 2000 of the matchingcontour within the thresholded image are obtained, and the edges of thedisplay within the captured images are thus located as the area withinthe four vertices that correspond to corners of the display 1400.Depending upon specific geometrical features of displays associated withparticular mobile device manufacturers (e.g. rounded corners, notchesintruding into displays, etc.) the edge boundaries image may be furtherrefined using the known geometrical tolerances of the manufacturersdevice specifications.

In a second technique for locating the edges corresponding to a display1400, fiducials with a predetermined color (as shown in referencenumerals 2101-2104 in FIG. 21) are output (FIG. 17, 1735) to the display1400 of the mobile device 800 just prior to image capture. Placement offiducials 2101-2104 appear proximate to the corner vertices of thedisplay 1400, and image analysis techniques may be applied to determinethe edges of the display of the mobile device within the captured image.Portions of the image are extracted using color range, and since thecolor of the fiducial marks is known, elements of the image appearingwithin a predefined tolerance value of the color of the fiducials appearin the extracted image. Next, polygons matching the respective shapes ofthe fiducials 2101-2104 are found through template matching approachesin the image, locating, for example, shapes 2301-2304 in FIG. 24, andthe location of the corresponding corner vertices in the captured imagecan be determined by the respective locations of the corner vertices inthe captured image (see FIG. 22). As with the first edge-detectiontechnique mentioned immediately above, the edges of the display withinthe captured images are thus located as the area within the fourvertices that correspond to corners of the display 1400. In analternative embodiment, any kind of fiducial may be used to assist withidentification of a component of the mobile device, such as bydisplaying a fiducial centered on a display of the mobile device, andthen in conjunction with aspect ratios of display geometry associatedwith the device (looked up, for instance, from a manufacturer databaseof known mobile devices with associated specifications) then displayedges may be refined by locating the search boundary as centered by theficucial, and expanding to meet the outward boundaries using thegeometry (and aspect ratios) of the associated mobile device. Thismethod may be further refined if the fidicuals ouput on the display ofthe mobile device are scaled to a known dimensions, (such as a crosswith each side being a known dimension such as 1 centimeter), then theedges may be deduced by obtaining a scale factor with the depicted sizeof the fiducial scale in the image, then close tolerances to the outerboundaries of the display can be identified by applying the scale factorto the dimensions of the manufacturer's specifications for the mobiledevice's display edges. Further, the shape of the fiducial (such asorthogonal intersecting lines) may be used to de-skew the image so thatedges can be better found that contain orthogonal geometries (such asrectangular sides).

Those of skill in the art further appreciate that while the word“display” or “screen” as it is used in the application generallyincludes an area where the mobile device may output a visual image, inother implementations of the present invention, the “display” or“screen” may include additional desired sections of the mobile devicewhere visually detectable defects may be found. More particularly, insome mobile phone models, a first area may exist proximate to and abovethe active display area, and a second area may exist proximate to andbelow the active display area of the mobile device that is covered by adamageable material (such as hardened glass). While these proximatefirst and second areas may not be capable of rendering visual output(for example in older iPhone devices with speaker/camera/sensor orificesin the first area and a home button or microphone orifice in the secondarea), portions of these areas nonetheless may be damaged and it is afeature of the present invention to extend image analysis of the“display” or “screen” of the mobile device to these proximal first andsecond areas to detect damage in those areas as well. In otherembodiments, a “notch area” removed from the active portion of a displaymay be included in the display analysis, and, for that matter, anydesired area or surface of the mobile device where visual defectdetection is desired. Additional tested areas of the mobile device,accordingly, may pertain to a back surface of the mobile device, a logoarea of the mobile device, a side area of the mobile device a bezel areaof the mobile device, or any other area for which a visual defectdetection is desired. In one embodiment, a number of defects such asscratches or cracks that are found within a predetermined proximity to,or otherwise covering or touching a logo area of a mobile device (forinstance, the “apple” logo on an iPhone model) may be determined andoutput upon defect analysis. Further, in various embodiments of thepresent invention, specific defects in the bezel of the mobile device,such as cracks, indentations, or pitting may be detected by visualanalysis of an image of the mobile device. Images of the mobile devicemay be obtained by any desired method, such as by holding any surface ofthe mobile device in front of a reflecting device, capturing an image bya buddy or companion device equipped with a camera, or placing/rotatingthe mobile device in front of a mounted camera or other device thatcaptures images (and/or video representations) of every surface of themobile device and may optionally create a 3-d image representation ofthe mobile device. Further, defects may include any kind of visuallydetectable issues; in addition to cracks and scratches, displaybrightness variations, color variations (such as an undesired pink hue),LCD bleed, color failures, and any other desired type of visual defectsmay also be detected and identified. Additionally, crack/scratch depth,width, and/or other visual appearance criteria can be associated withidentified cracks/scratches, and reported with an intensity level toassist with classifying the impact of the identified crack/scratch onthe mobile device's value.

Now that edges of the display 1400 can be deduced from the locations ofthe corner vertices in the captured image, the area of the capturedimage corresponding to the display area can be extracted as a sub-imageand transformed to a rectangular shape in a single step. This isaccomplished in one embodiment through calculating the moments of thedisplay shape in the image and perspective transforming the displayportion to a rectangular image through a technique known to those ofskill in the art (an example of the perspective transform implementationcan be found athttps://www.pyimagesearch.com/2014/08/25/4-point-opencv-getperspective-transform-example/,the disclosure of which is fully incorporated by reference herein forall purposes).

Once the sub-image corresponding to the display 1400 from the capturedimage has been obtained, additional resizing/rescaling/resampling areundertaken so that the pixel dimensions of the sub-image are acceptableas input to the neural network 47 of the present invention. Thesub-image can be resampled to any desired pixel configuration, and inone embodiment, the image is resampled to a 320×544 pixel configuration(width, by height, in this example, corresponding to the portraitorientation of the sub-image). An example of a perspective-transformedsubimage is shown in reference numeral 1400A of FIG. 20A, correspondingto the display portion of the captured image 1400 shown in FIG. 19.Defect 1900 appears in both images 1400, 1400A.

Step 1830 illustrates the analysis of the rescaled sub-image by a neuralnetwork to identify defects in the mobile device 800, and in particular,defects in the display 1400 of the mobile device. The rescaled andperspective-transformed sub-image (for instance, 1400A in FIG. 20A) isapplied to the input of a convolutional neural network which has beenpreviously trained to detect defects with training data comprisingdisplays and accompanying defect classes. Once the network analyzes theimage, an output is produced that identifies a defect, such with acolored polygon 1900A shown in FIG. 20B. The colored polygon drawsattention to the defect found by the neural network, and as mentionedpreviously, additional outputs and analysis such as heat maps may beprovided based on the particular network architecture. More illustrativeexamples follow in FIGS. 20C-E, where input sub-image (FIG. 20C, 1400A)with defect (FIG. 20C, 1900) is input to neural network 47 of thepresent invention, to produce an output (FIG. 20E, 1400B) with defectcircumscribed by a polygon (FIG. 20E, 1900A), and a heat map outputversion (FIG. 20D, 1400D), depicting a heat map 1900D corresponding toactivation classes for detected defects (FIG. 20C, 1900) in the displaysub-image (FIG. 20C, 1400A). Another example is provided with a secondinput sub-image (FIG. 20F, 1400F), producing output (FIG. 20G, 1400G)with heat map indicating location of detected defect class (FIG. 20G,1900G), and an output (FIG. 20H, 1400H) with defect circumscribed by apolygon (FIG. 20H, 1900H). Those of skill in the relevant artsunderstand that many other types of outputs are possible, and differentdefect types as explained previously may be detected though anappropriately trained CNN architecture.

FIG. 16A illustrates another embodiment of the present invention, andwhile certain analytical steps as described in regards to FIG. 16 may beimplemented identically, embodiments of the present invention in regardsto FIG. 16A utilize alternative approaches. More particularly, in oneembodiment, the present invention may allow defects associated with amobile device to be detected without having to first install a FaultState Test Application (FSTA). More particularly, in step 1610A, contactis initiated between the mobile device to be tested and a remotediagnostic service (RDS) such as a web server specifically configuredwith software and necessary communications circuitry to connect to themobile device and remotely execute tests to determine whether defectscan be found within the mobile device. Such contacts may be initiated inany desired manner, including by a data-based connection to a webaddress from the mobile device, the web address corresponding to adevice diagnostic server, and wherein the mobile device user is promptedto enter identifying information, and the user test profile for thespecific mobile device may be created and stored in a databaseassociated with the web server along with diagnostic results. Thedevice. When the connection is initiated, and requested information isentered by the mobile device user, the user of the mobile device 800indicates to the remote diagnostic service that defect testing is tocommence. In one embodiment, the remote diagnostic service begins imagecapture process 1620A in a manner similar to the steps described inregards to FIG. 16; however, alternative embodiments include activatinga webcam on the mobile device or a companion device to capture an imageof the mobile device under test either directly or through a companiondevice; likewise, on the mobile device under test, a browser runningfrom the RDS can open that device's rear-facing camera, then capture animage from either a front-facing or a rear-facing camera in view of areflecting surface. If a companion device is used in conjunction withthe mobile device to be tested, a websock connection may be initiatedbetween the two devices to allow inter-device communication to commandthe devices to display and capture images. In this way, the mobiledevice under test may be remotely instructed by the RDS, through abrowser on the mobile device, to capture a picture of itself in areflecting device. Therefore, the RDS can facilitate image capture froma webcam, from either a front-facing or rear-facing camera of the mobiledevice itself, from a companion device also in communication with theRDS, and other methods where the RDS may initiate image capture. Thecaptured images are then 1630A communicated through a communicationslink between the RDS and the mobile device (such as a connection to theweb server previously established), where the RDS initiates analysis ina manner similar to the steps shown 1635, 1640, of FIG. 16. Those ofskill in the relevant arts understand than the RDS may also beimplemented with intelligent agents that are capable of interacting withthe mobile device through a network, and thus conducting tests andobtaining information to support defect analysis regarding such mobiledevices.

Additional defect testing (e.g. 1642A) may be conducted with aspects ofthe mobile device under test, whether associated with an RDS, or whethera FSTA was installed to conduct testing. For example, some tests areexecuted (either by the FSTA or remotely via a connection between themobile device and the RDS) to determine, for example: (1) vibration test(determining, for example, that the device's vibrating agitator isfunctional); (2) Speaker test; (3) screen touch test; (4) front cameratest; (5) back camera test; (5) screen pressure test; (6) accelerometertest; (7) gyroscope test; (8) battery state, including operationalcapacity, charging time, and whether the battery is swollen (thusindicating a potential defect/hazard state); and (9) recognition ofmixed model/provenance, including association of a unique fingerprint orID of the mobile device. Further, aspects of the present inventionprovide for sonic analysis to determine defects associated with themobile device.

Through sonic analysis, a vibrational signal (such as an audio signal orknown frequency mechanical vibration) is created and applied to themobile device, and the microphone of the mobile device “listens” to whatcan be detected as a result. The captured audio signal from themicrophone can then be analyzed to determine whether certain kinds ofdefects may exist in the mobile device. For example, a predeterminedsignal may be output from a speaker of the mobile device, (oralternatively through a vibration or vibration pattern induced by anagitator located in the mobile device) then the mobile device listens,though a microphone associated with the mobile device (or a companiondevice) for audio signatures received by the microphone of the mobiledevice; in one version, simple audio testing of various frequenciescould identify whether the speaker/microphone pair are functional at thedesired range of frequencies; further, spectral analysis of the audiosignal captured by the microphone may identify certain defects in thespeaker or microphone. The spectral analysis my further be compared withideal propagation through a manufacturer-identical exemplar where soundpropagation was classified, and defects such as cracks in glass attachedto the mobile device may cause audio propagation discontinuities orrattles that may be deduced from the audio signal received from themicrophone. Certain mechanical vibration patterns may also be created bythe agitator to attempt to find resonant frequency rattles with looseparts within the mobile device that may be sensed by the received audiopattern of the device's microphone. Such spectral analysis may beperformed in concert with neural network implementations that have beenpre-trained to identify certain audio anomalies that correspond todefects that may be associated with the mobile device.

Through a process discussed in regards to FIG. 17, an image of thedisplay of the mobile device 800 is captured 1620, and the capturedimage is optionally pre-processed 1625 by rotating the image to aportrait mode and/or fine rotating to reduce skew, and/or cropping theimage to a desired aspect ratio. The captured (and optionallypre-processed) image is then uploaded 1630 to a server (such as server860), and as discussed further in regards to FIG. 18, the uploaded imageis processed 1635 to determine whether any cracks or other types defectsare present. Alternatively, or in combination, the extent andcharacteristics of the screen cracks or defects are characterized by theprocessing step 1635 for further analysis and reporting. Once theanalysis 1635 of any potential defects is complete, the resultinginformation is formatted 1640 for presentation, storage, or display toany user for any necessary purpose, such as for assessment of whetherthe mobile device 800 is in proper working condition. The process thenexits 1645.

FIG. 24 shows one exemplary data flow through a block diagram 2400 of aconvolutional neural network (CNN) architecture of the presentinvention. Before presentation of device image or data 2405, thecomponents of the CNN have been trained to recognize particular devicedefect classes, including defects such as scratches, cracks, and breaksin a device display. In one embodiment, the downscaled and resampleddevice image (for example, an image file as resulting from step 1820 ofFIG. 18), along with any optional device data that may be used inprocessing an image associated with the device (such as a uniqueidentifier associated with the device, including but not limited to anIMEI associated with the device) are presented 2405 to an input layer2410 of the neural network. The input layer is communicatively coupledto block 2420, comprising at least one convolutional layer, a poolinglayer, and a fully connected layer as conventionally implemented in aCNN. The trained CNN operates upon the input data and produces an outputcorresponding to an output showing any detected defect class 2430.Operation may end at that point with analysis of output of block 2430,or may continue to additional processing through input to a second inputlayer 2440. The defects in block 2430 may be presented to block 2450,where a de-convolutional layer, un-pooling layer, and fully connectedlayer further process and highlight features of the input imagecorresponding to locations where defects were located. An output layer2460 provides output in the form of diagnostic images 2470, which may bestored in a database (for example, FIG. 8, 880) and associated in thedatabase 880 with device data identifying the device image analyzed bythe network 2400.

Generally, to increase the performance and learning capabilities of CNNmodels, the number of different network layers in the CNN can beselectively increased. The number of intermediate distinct layers fromthe input layer to the output layer can become very large, therebyincreasing the complexity of the architecture of the CNN model. Thepresent disclosure employs learning CNN models, to identify devicedefects and/or create visual maps indicating presence of a defectlocated within an image of a mobile device. Consistent with thedisclosed embodiments, CNN system 2400 may be configured to generateoutputs showing defects (such as identified defect 1900A in devicedisplay 1400B of FIG. 20E, or identified defect 1900H of display 1400Hof FIG. 20H). Alternatively embodiments of the present invention providethat CNN system 2400 may be configured to generate outputs showing heatmaps corresponding to areas of an image that corresponding likelihood ofa detected defect class (such as identified heat map 1900D in devicedisplay 1400D of FIG. 20D, or identified heat map 1900G of display 1400Gof FIG. 20G). For further illustration, the image 1400A in FIG. 20Cwould be presented as device image/data 2405 of network 2400, and FIGS.20D and/or 20E would be obtained from the outputs of the CNN 2400.Similarly, the image 1400F of FIG. 20F would be presented as deviceimage/data 2405 of network 2400, and FIG. 20G and/or 20H would beobtained from the outputs of the CNN 2400. As explained above, dependingon the particular embodiment, outputs may be obtained from either ofblocks 2430 or 2470 of network 2400.

As used herein, and as explained above, a CNN model used by thedisclosed system and method of the present invention may refer to anyneural network model formulated, adapted, or modified based on aframework of convolutional neural network. For example, a CNN model usedto identify device defects in embodiments of the present disclosure mayselectively include intermediate layers between the input and outputlayers, such as one or more convolutional/de-convolutional layers,non-linear operator layers, pooling or subsampling layers, fullyconnected layers, final loss layers, deconvolution layers, and/orunpooling or upsampling layers.

In an alternate embodiment, outputs from the CNN model may be trained toproduce either a heat map or a specific defect identification withoutdeconvolutional layers without needing further processing after block2430. Accordingly, in one embodiment, outputs are created through classactivation maps through training output layers of a neural network ofthe present invention, and in one embodiment, heat maps may be output asdescribed previously. In one aspect, heat maps are generated throughobtaining mapping functions on each section of an image being output bya trained neural network of the present invention. Mapping functionswithin each subsection of an image may use scores on the subsections ofthe image to identify specific defect classes when the trained networkhas detected a defect in the image data. In one implementation of thepresent invention, a heat map may be obtained from a particular layerwithin the CNN model itself. As those of skill in the neural networktraining arts understand, for example, a softmax function may be used inthe final layer of a CNN defect classifier, and may be used to generatethe heat map by computing the resulting layer element scores, producethe output into shape of the image data and overlay that upon the areacorresponding to the test image to identify a heat map. Alternatively,in one embodiment, the CNN may reduce or strip down aspects of the inputdata image, apply different convolution layers, capture the outputs ofone of the convolution layers, (output across the entire image), andproduce a score for each part of the image, which in turn produces adefect activation map. The areas that exceed a predetermined level ofactivation may be converted in output image data to a specific color,for instance red, to identify defects found on the test image (forinstance, read color to an area of the image corresponding to a displaycrack in the test image). As a further aspect, in the network outputlayers, a threshold (compared to a predetermined color threshold value)may be performed on the image data to find the red portion of the image,then a minimum bounding rectangle may be placed around the red area tomore particularly identify the defect area.

FIG. 25 presents an overview of a flow diagram 2500 for an embodiment ofthe present invention, including three phases: a training preparationphase 2525, a network training phase 2526, and a network inference phase2527. As discussed in brief previously, disclosed systems and methodsfor identifying device defects generally include several stages thatwill be discussed in turn; for example a training stage that “trains” or“learns” a CNN model using training datasets, and a prediction orinference stage in which the trained CNN model uses the trainingdatasets and input data to identify device defects (and/or defectclasses). As mentioned above, the aforementioned training stagesgenerally correlate with the network training 2526 as depicted in FIG.25. As used herein, “training” a CNN model refers to determining one ormore parameters of at least one layer in the CNN model. For example, aconvolutional layer of a CNN model may include at least one filter orkernel. One or more parameters, such as kernel weights, size, shape, andstructure, of the at least one filter may be determined by e.g., aback-propagation-based training process. Further, the aforementioneddescription regarding FIG. 24 generally correlates with networkinference 2527 as depicted in FIG. 25. As used with respect to theembodiment described herein, “predicting” from a CNN model refers toidentifying device defects from an image of a device using any desiredtype of a processed image of the device 2510, including in oneembodiment, an image of a display of the device.

In the training preparation phase 2525, training inputs 2520 areprepared and processed for use in training the CNN neural networkcomponent (e.g., FIG. 8, 47). A network model is selected 2505 for usein training; such model selection may include, for example,identification of a convolutional neural network architecture andappropriate processing layer configuration. The neural network componentmodel (FIG. 8, 47) is initialized with an initial layer configuration,an initial connection configuration, a set of weights, and a set ofbiases. In addition to device images 2510 and respective expectedresults (also known to those of skill in the relevant arts as “groundtruth” data) 2515, other parameters (as described more fully below) maybe specified for input as training data 2520, such as identifyinginformation for each respective device image. In an embodiment, trainingdata 2520 may also include data for actual defective devices orsimulated defective devices. In another embodiment, training data 2520may be synthetically created, e.g. computer-generated images based upontheoretically possible defect types situations or devised for purposesof model testing.

In various embodiments, and in a manner similar in context with process1800 of FIG. 18, the preparation phase 2525 includes resampling allimaging data in the training data 2520 to a common grid size and gridspacing. Then, image pixel values can optionally be resampled to commonscale across all image data in the training data 2720 to improvelearning performance and convergence based on training data furnished tothe network. After data preparation, training data and testing datarepresent device images and respectively corresponding identifieddefects comprising the ground truth data.

The network training phase 2526 commences with training data 2520 beingpresented to the configured and initialized neural network model fromstep 2505 (with no input specified). The neural network component (FIG.8, 47) estimates results 2535 from the patient image data and producesestimated results, for example, an identified defect class. A comparisonis then made between the estimated results from step 2535 and theexpected results 2515 corresponding to the device images 2510, and anerror map (“training error”) is generated based on differences betweenthe expected results 2515 and the estimated results 2535. The error mapis compared to evaluation criteria 2545. For example, a loss functionsuch as a mean absolute error function (MAE), or mean squared error(MSE) function, can be used to determine whether error criteria are met.One skilled in the art would be familiar with various loss functions,such as the mean absolute error (MAE) of the model prediction or L₁ normJ(θ*)=arg min_(θ)∥Y=Y*∥₁, or the mean squared error (MSE) or L₂ normJ(θ*)=arg min_(θ)∥Y=Y*∥₂ where θ* comprises the choice of parametersthat minimizes the differences between Y and Y*. A back-propagationalgorithm may be used to compute the gradient of the error function withrespect to the model parameters or weights. Then θ may be updatediteratively using a stochastic gradient descent algorithm to converge onθ*.

If the errors do not satisfy the error threshold criterion, the modelparameters of the neural network (FIG. 8, 47) (e.g., weights and biases)are updated 2550, e.g. to minimize error according to a learningalgorithm (e.g. a regularized gradient descent optimization approach),and the training data is re-presented 2530 to the neural network (FIG.8, 47) with the neural network's newly-assigned model parameters.

In one embodiment, determining whether criteria are met 2545 includespresenting one or more test device images that are different from thetraining images (but have no defects pre-identified) to the networkmodel as currently configured to generate an estimate of defectsrespectively corresponding to such test device images. The resultingdefect classifications can then be compared with the groundtruth/expected defect classifications to assess the quality of thenetwork parameter model. The differences between the expected defectsand the estimated defects for this test data is the “test error.” In anyevent, iteration from step 2550 to step 2530, to steps 2535 and 2540, tostep 2545 continues until the error threshold criterion is met,whereupon the training process 2525 concludes 2555, with a trainedneural network component model ready for use with captured images ofreal devices and corresponding data (FIG. 8, 45, 46).

In an alternative embodiment, the neural network (FIG. 8, 47) may betrained in multiple steps and various portions of the neural network maybe trained with separate training data sets to achieve particular defectclassification. For example, a convolutional neural network of thepresent invention may be pre-trained to recognize a large number ofgeneric visual object classes, and then a final layer of the network maybe retrained to identify specific visual items, such as screen defects(e.g., cracked or damages displays of a mobile device). In one versionof the present invention, characteristics regarding the defect (e.g. adisplay crack) may be provided in the training data associated withtraining images, and may be provided in various embodiment withoutcoordinates of where such crack may exist in the training data image,and in still other embodiments, the training data images that showdefects (e.g. display cracks) are accompanied by coordinates identifyingthe cracked areas within the image so that training of the output layermay be optimized. Using multiple training passes, such as training anetwork with a large number of visual classes then retraining a finallayer of the network with specific defects to be detected may provideembodiments with advantages of efficiency over retraining an entirenetwork if the training data 2560 is updated or changed with new defecttypes or updated amounts of training examples.

Once trained, the neural network component model (FIG. 8, 47) may bestored in a memory such as the server memory (e.g., FIG. 8, 16), withinnon-transitory memory of the network component (FIG. 8, 47) or in adatabase (e.g. FIG. 8, 880). The trained network model may then be usedin the network inference steps 2527 to compute a useful output (e.g.classified defects associated with a device) when presented with mobiledevice images and/or data (FIG. 8, 45, 46) for which defect analysis isdesired. The trained neural network is configured 2565, for example, byloading stored weights and biases for the particular trained networkconfiguration. Patient data inputs are presented to the trained andconfigured neural network component (FIG. 8, 47) to obtain predictedresults. In a preferred embodiment, predicted results comprise one of adefect classification with identified defects, or a defectclassification with a displayed heat map. Thus, for the device ofinterest, device images and/or data 2560 are presented to the trainedand configured neural network model 2570 to produce the predictedresults 2575. In a further embodiment, the predicted results are in turnreviewed 2580 by any desired entity, such as for use in determining theinsurability of a mobile device or validity of an insurance claim forthe mobile device.

The particular implementations shown and described above areillustrative of the invention and its best mode and are not intended tootherwise limit the scope of the present invention in any way. Indeed,for the sake of brevity, conventional data storage, data transmission,and other functional aspects of the systems may not be described indetail. Methods illustrated in the various figures may include more,fewer, or other steps. Additionally, steps may be performed in anysuitable order without departing from the scope of the invention.Furthermore, the connecting lines shown in the various figures areintended to represent exemplary functional relationships and/or physicalcouplings between the various elements. Many alternative or additionalfunctional relationships or physical connections may be present in apractical system.

Changes and modifications may be made to the disclosed embodimentswithout departing from the scope of the present invention. These andother changes or modifications are intended to be included within thescope of the present invention, as expressed in the following claims.

What is claimed is:
 1. A method comprising: installing an executableprogram on a mobile device; prompting a user of the mobile device toplace the mobile device so that a display section of the mobile devicefaces a reflective surface of a mirror; performing a trackingcalibration function to adjust the mobile device position and displayfor optimal image capture; modifying a brightness setting and a camerasensitivity setting of the mobile device to optimize image capture;determining that the mobile device is in an acceptable orientation withrespect to the mirror, and thereupon: capturing an image of the mobiledevice as reflected from the mirror; and processing the captured imageto determine whether a defect can be identified in the display of themobile device.
 2. The method of claim 1, further comprising formattingresults of the determining step for transmission to a host server. 3.The method of claim 1 wherein the tracking calibration function furthercomprises: retrieving one or more ambient lighting parameters from themobile device; adjusting a brightness parameter of the display of themobile device based upon the retrieved ambient lighting parameters;adjusting a sensitivity parameter of the camera of the mobile devicefrom the analyzed ambient lighting settings; determining from cameradata obtained from the mobile device that the mobile device is out ofoptimal placement, whereupon: a direction for the user to move themobile device to obtain an optimal placement of the mobile device withrespect to the mirror is calculated; and the direction is displayed onthe mobile device in a manner readable by the user in the mirror.
 4. Themethod of claim 3 wherein adjusting a sensitivity parameter of thecamera of the mobile device further comprises: adjusting an ISO settingof the camera; and adjusting a shutter speed of the camera.
 5. Themethod of claim 3 wherein adjusting a sensitivity parameter of thecamera of the mobile device further comprises adjusting an exposurecompensation value associated with the camera of the mobile device. 6.The method of claim 3 further comprising: displaying one or morefiducials on the display of the mobile device; and sensing one or moreof the fiducials from camera data obtained from the mobile device. 7.The method of claim 3 wherein: retrieving one or more ambient lightingparameters from the mobile device further comprises obtaining theparameters from an API associated with an operating system installed onthe mobile device.
 8. The method of claim 1, wherein processing thecaptured image further comprises: rotating the captured image to aportrait mode; and cropping the image.
 9. The method of claim 8,wherein: the rotating and cropping steps are performed by a processor ofthe mobile device; and the rotated and cropped image is furtherformatted for transmission to a host server.
 10. The method of claim 1,wherein processing the captured image to determine whether a defect canbe identified in the display of the mobile device further comprises:extracting, from the captured image, a subimage corresponding to themobile device screen; transforming the perspective of the subimage intoa rectangular aspect; resampling the transformed subimage to apredetermined input image data configuration; presenting the resampledtransformed subimage to a pre-trained neural network to identify one ormore device defects; and obtaining from the neural network an indicationof a first defect from the one or more device defects that correspondsto the screen of the mobile device.
 11. The method of claim 10, whereinobtaining from the neural network an indication of a defect furthercomprises obtaining, from the neural network, an indicia showing aportion of the mobile device screen that activates a defective class inthe pre-trained neural model.
 12. The method of claim 10, whereinobtaining from the neural network an indication of a first defectfurther comprises identifying a local area of a fault in the display ofthe mobile device using class activation mapping.
 13. The method ofclaim 10, further comprising: receiving, by a server communicativelycoupled to the mobile device, the captured image, and wherein theprocessing of the captured image is performed within a processor of theserver.
 14. The method of claim 10, further comprising training thepre-trained neural network with training data stored in a database inthe server, the training data comprising at least: device screentraining images and identified defects respectively corresponding to thedevice screen training images.
 15. The method of claim 14, wherein thetraining data stored in the database is augmented with a new trainingimage corresponding to a captured image of the mobile device and acorresponding identified defect associated with the captured image ofthe mobile device.
 16. The method of claim 15, further comprisingre-training the pre-trained neural network with the augmented trainingdata.
 17. The method of claim 10, wherein the predetermined input imagedata configuration comprises a pixel configuration of 320×544 pixels.18. The method of claim 10, wherein the device defect comprises one ormore of: a crack in the display of the mobile device; one or more stuckpixels in the display of the mobile device; one or more dead pixels inthe display of the mobile device; blemish or crack on a casing of themobile device; LCD bleed on the display of the mobile device; a pink huedefect on the display of the mobile device; and combinations thereof.19. A system comprising: a mobile device, the device comprising: aprocessor in communication with a memory; a user interface incommunication with the processor, the user interface including atouch-sensitive display and a data entry interface; a communicationsmodule in communication with the processor and configured to provide acommunications interface to a host server, the host server including aserver processor communicatively connected to a database, a servercommunications interface, a server user interface, and a server memory;wherein the memory of the mobile device includes instructions that whenexecuted by the processor cause the mobile device to perform the stepsof: prompting a user of the mobile device to place the mobile device sothat a display section of the mobile device faces a reflective surfaceof a mirror; performing a tracking calibration function to adjust themobile device position and display for optimal image capture; modifyinga brightness setting and a camera sensitivity setting of the mobiledevice to optimize image capture; determining that the mobile device isin an acceptable orientation with respect to the mirror, and thereupon:capturing an image of the mobile device as reflected from the mirror;and formatting the captured image for transmission to the server fordefect analysis.
 20. The system of claim 19 wherein the trackingcalibration function further comprises: retrieving one or more ambientlighting parameters from the mobile device; adjusting a brightnessparameter of the display of the mobile device based upon the retrievedambient lighting parameters; adjusting a sensitivity parameter of thecamera of the mobile device from the analyzed ambient lighting settings;determining from camera data obtained from the mobile device that themobile device is out of optimal placement, whereupon: a direction forthe user to move the mobile device to obtain an optimal placement of themobile device with respect to the mirror is calculated; and thedirection is displayed on the mobile device in a manner readable by theuser in the mirror.
 21. The system of claim 20 wherein adjusting asensitivity parameter of the camera of the mobile device furthercomprises: adjusting an ISO setting of the camera; and adjusting ashutter speed of the camera.
 22. The system of claim 20 whereinadjusting a sensitivity parameter of the camera of the mobile devicefurther comprises adjusting an exposure compensation value associatedwith the camera of the mobile device.
 23. The system of claim 20 furthercomprising: displaying one or more fiducials on the display of themobile device; and sensing one or more of the fiducials from camera dataobtained from the mobile device.
 24. The system of claim 20 wherein:retrieving one or more ambient lighting parameters from the mobiledevice further comprises obtaining the parameters from an API associatedwith an operating system installed on the mobile device.
 25. The systemof claim 19, wherein formatting the captured image further comprises:rotating the captured image to a portrait mode; and cropping the image.26. The method of claim 1, wherein the server memory includesinstructions that when executed by the server processor cause the serverto perform the steps of: receiving the captured image from the servercommunications interface; extracting, from the captured image, asubimage corresponding to the mobile device screen; transforming theperspective of the subimage into a rectangular aspect; resampling thetransformed subimage to a predetermined input image data configuration;presenting the resampled transformed subimage to a pre-trained neuralnetwork to identify one or more device defects; and obtaining from theneural network an indication of a device defect that corresponds to thescreen of the mobile device.
 27. The system of claim 26, whereinobtaining from the neural network an indication of a defect furthercomprises obtaining, from the neural network, an indicia showing aportion of the mobile device screen that activates a defective class inthe pre-trained neural model.
 28. The system of claim 26, whereinobtaining from the neural network an indication of a device defectfurther comprises identifying a local area of a fault in the display ofthe mobile device using class activation mapping.
 29. The system ofclaim 26, further comprising training the pre-trained neural networkwith training data stored in a database in the server, the training datacomprising at least: device screen training images and identifieddefects respectively corresponding to the device screen training images.30. The system of claim 29, wherein the training data stored in thedatabase is augmented with a new training image corresponding to acaptured image of the mobile device and a corresponding identifieddefect associated with the captured image of the mobile device.
 31. Thesystem of claim 30, further comprising re-training the pre-trainedneural network with the augmented training data.
 32. The system of claim26, wherein the predetermined input image data configuration comprises apixel configuration of 320×544 pixels.
 33. The system of claim 26,wherein the device defect comprises one or more of: a crack in thedisplay of the mobile device; one or more stuck pixels in the display ofthe mobile device; one or more dead pixels in the display of the mobiledevice; blemish or crack on a casing of the mobile device; LCD bleed onthe display of the mobile device; a pink hue defect on the display ofthe mobile device; and combinations thereof.
 34. The system of claim 26,wherein the neural network comprises a convolutional neural networkarchitecture.
 35. The system of claim 26, wherein the neural network maybe implemented: through software stored in the server memory, throughexternal hardware communicatively coupled to the server processor, orthrough a combination of stored software and external hardware.